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owainlewis/awesome-artificial-intelligence

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TLDR

A curated list of AI books, courses, landmark research papers, tools, and frameworks organized by topic and skill level, a starting-point index for engineers building AI products and beginners exploring the field.

Mindmap

mindmap
  root((repo))
    What it does
      Curated AI resources
      Books and courses index
      Papers reference
      Tools directory
    Topics
      RAG and agents
      Evaluation methods
      Generative models
      Foundational theory
    Skill Levels
      Beginner courses
      Advanced Stanford MIT
      Research papers
    Tools Covered
      AI models
      Coding assistants
      Image and audio gen
    Audience
      AI engineers
      Beginners learning AI
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Code map

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Things people build with this

USE CASE 1

Find beginner-friendly AI courses from Google and Hugging Face to start learning without prior experience

USE CASE 2

Discover the key research papers behind modern AI architectures like transformers and diffusion models

USE CASE 3

Locate tools and frameworks for retrieval-augmented generation to add private document search to an AI product

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

This repository is a curated collection of resources for people learning about or working in artificial intelligence. It covers books, online courses, landmark research papers, newsletters, frameworks, and tools, organized into sections by topic and skill level. The collection focuses on what the maintainer calls AI engineering: the practical work of building production AI systems, including retrieval-augmented generation (a technique for giving AI models access to private documents), agent frameworks, evaluation methods, and deployment. Alongside those applied topics, it also links to foundational academic material for people who want deeper theoretical grounding. The books section includes both modern practical titles covering machine learning pipelines, generative models, and end-to-end AI product building, as well as classic foundational texts on neural networks, reinforcement learning, and general AI theory. The courses section is split by difficulty level, with beginner options from Google and Hugging Face and more advanced material from Stanford and MIT. A separate papers section lists the key research that shaped the architectures and techniques in widespread use today. The tools section covers currently active AI models, coding assistants, image and video generation services, and audio tools. Each entry includes a one-line note on what the tool is best suited for, making it useful as a quick orientation map rather than a deep technical guide. The repository contains no code of its own. It is a living reference document that the maintainer updates over time. Its focus has shifted toward practical AI system building, and it now functions primarily as a starting-point index for engineers building AI products rather than a survey of academic research.

Copy-paste prompts

Prompt 1
I want to build a production RAG system. Which frameworks and resources in awesome-artificial-intelligence should I start with, and in what order?
Prompt 2
Point me to the foundational research papers in this list that I need to read to understand how modern large language models work.
Prompt 3
Which beginner AI courses from awesome-artificial-intelligence would you recommend for a software developer with no machine learning background?
Prompt 4
I need an AI coding assistant and an image generation tool for my workflow. Which tools in the list best fit those two needs?
Prompt 5
What evaluation frameworks or methods does this list recommend for testing the quality of AI model outputs?
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